# Memory compression in the hippocampus

> **NIH NIH F31** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2020 · $45,520

## Abstract

Project Summary / Abstract
 The brain receives a constant stream of information about the world that it must organize and encode
into memory. Notably many of the episodes we experience are highly correlated, meaning features are redundant
across memories and hence compressible. This proposal aims to test a theory of memory compression in the
hippocampus, a structure critical for learning and memory in the mammalian brain. The model suggests that the
hippocampal network can leverage the correlations between experiences in order to build compressed neural
representations, which support efficient encoding of episodic memories. It provides a natural explanation for the
observation of place cells in recordings of rodent hippocampal neurons, as repeated visits to a physical location
will be described by highly redundant and compressible sensory and behavioral parameters. Further, the model
predicts that the experimentally observed instability in hippocampal coding arises due to ongoing plasticity in the
network, as it continually adjusts its internal representations to compress new inputs. Specifically, fluctuations in
hippocampal coding should encode information about the recent history of experience, as they reflect the most
recent synaptic changes induced by prior episodes.
 I will use in vivo 2-photon calcium imaging in behaving mice and computational modeling to test whether
the hippocampus builds compressed representations of experience, and evaluate specific predictions of the
model. In Aim 1, I will study this question in the context of olfactory coding, to measure whether correlations
between sensory stimuli affect the similarity of their representations in the hippocampus. Recent work has
demonstrated that hippocampal activity also encodes information about the correlations between episodes in
time. In Aim 2, I will extend the compression model to incorporate temporal correlations, and compare simulations
to neural data during a trace conditioning paradigm in order to assess whether the hippocampus adaptively
compresses information about the temporal structure of experience. In Aim 3, I will analyze variability in
hippocampal place coding, in order to test the hypothesis that the fluctuations of place cells are history dependent
– that is, they can be used to decode information about episodes in the recent past. These experiments will test
explicit predictions of a highly novel theory of hippocampal function. Evidence for this framework could provide
a unifying account of many spatial and non-spatial coding properties of hippocampal neurons, and spur the
development of new theories of hippocampal and cortical roles in memory encoding.

## Key facts

- **NIH application ID:** 9991637
- **Project number:** 5F31MH121058-02
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** James Benjamin Priestley
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $45,520
- **Award type:** 5
- **Project period:** 2019-09-01 → 2021-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9991637

## Citation

> US National Institutes of Health, RePORTER application 9991637, Memory compression in the hippocampus (5F31MH121058-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9991637. Licensed CC0.

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